9th November 2017

Overview

Objectives

  • Characterise the HaCaT immunopeptidome \(\pm\)DNCB.
  • Characterise HaCaT protein turnover/abundance \(\pm\)DNCB.
  • Infer relationship(s) between DNCB, peptidome, protein turn over and protein abundance.
  • Identify (and test) candidate immunogenic peptides.

Method development

Capturing MHC I peptides | Observing protein turnover

Peptidome

Peptidome results 1

Venn diagram

Peptidome results 2

Modified peptide spectra

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Protein turnover

Experimental next steps

Complete HaCaT dataset | Start HLA-A2 transfectant dataset

Method development

Quantifying our uncertainty

Peptidome areas of uncertainty

  • How many peptides are there per treatment?
  • Is the size of the peptidome repertoire different between treatments?
  • Can we estimate the probability that a peptide is unique to one treatment?

Paul A. Smith, S3RI University of Southampton

Protein turnover

Binary general linear model

dat_turnover[c(1:5,902:907),1:3]
## # A tibble: 11 x 3
##    Peptide Treatment Turnover.Rate
##      <int>     <chr>         <dbl>
##  1       1   Control         0.149
##  2       0   Control         0.513
##  3       0   Control         0.491
##  4       1   Control         0.614
##  5       1   Control         0.445
##  6       0      DNCB         0.111
##  7       1      DNCB         0.794
##  8       1      DNCB         0.704
##  9       0      DNCB         0.268
## 10       0      DNCB         0.393
## 11       1      DNCB         0.802

Protein turnover

Binary general linear model

\(Y_i = \beta_0 + \beta_1.X_i + \epsilon_i\)

\(X_i\) : protein turnover rate for protein \(X\).

\(Y_i\) : probability of observing peptide from protein \(X\) at the cell surface.

\(\beta_0\) : expected value of \(Y_i\) under given treatment.

\(\beta_1\) : increase in probability of \(Y_i\) due to increase in turnover rate.

\(\epsilon_i\) : is the residual error term.

Models for \(\beta_0\) and \(\beta_1\)

Summary

Complete HaCaT dataset | Repeat with HLA-A2 transfectant dataset